Significant technological advances made in recent years have shepherded a dramatic
increase in utilization of digital technologies for biomedicine–everything from the …

Background Identifying patients at risk of not achieving meaningful gains in long-term
postsurgical patient-reported outcome measures (PROMs) is important for improving patient …

Objective Determine if deep learning detects sepsis earlier and more accurately than other
models. To evaluate model performance using implementation-oriented metrics that …

Personalized Medicine (PM) is rapidly advancing in everyday medical practice.
Technological advances allow researchers to reach patients more than ever with their …

Zack et al.(1) foreshadow the day when artificial intelligence and related big data constructs
have a prominent, if not predominant role in health care. The article describes the use of …

Thousands of clinical factors define a unique phenotype for each patient, all of which
contribute to the prognosis of that patient in different ways. This makes the problem of …

Sepsis is one of the biggest risks to patient safety, with a natural mortality rate between 25%
and 50%. It is difficult to diagnose, and no validated standard for diagnosis currently exists. A …

Personalized Medicine (PM) is rapidly advancing in everyday medical practice.
Technological advances allow researchers to reach patients more than ever with their …

In this thesis, we aim to use electronic health records (EHRs) to predict sepsis and in-
hospital mortality by using machine learning algorithms. We first explored EHRs dataset and …

The failure of several clinical trials in traumatic brain injury (TBI) targeting thresholds of
physiologic variables challenges the clinical utility of existing approaches and highlights the …